In my imperative-style Scala code, I have an algorithm:
def myProcessor(val items: List) {
var numProcessed = 0
while(numProcessed < items.size) {
val processedSoFar = items.size - numProcessed
numProcessed += processNextBlockOfItems(items, processedSoFar)
}
}
I would like to keep the "block processing" functionality, and not just do a "takeWhile" on the items list. How can I rewrite this in functional style?
You need to change it to a recursive style wherein you "pass" in the "state" of each loop
#tailrec
def myProcessor(items: List[A], count: Int = 0): Int = items match{
case Nil => count
case x :: xs =>
processNextBlockOfItems(items, count)
myProcessor(xs, count + 1)
}
assuming that "processedSoFar" is not an index. If you can work with the current "head" of the list:
#tailrec
def myProcessor(items: List[A], count: Int = 0): Int = items match{
case Nil => count
case x :: xs =>
process(x)
myProcessor(xs, count + 1)
}
where process would only process the current "head" of the List.
So, this depends on what you consider to be more functional, but here's a version without the 'var'
def myProcessorFunctional(items: List[Int]) {
def myProcessorHelper(items: List[Int], numProcessed: Int) {
if (numProcessed < items.size) {
val processedSoFar = items.size - numProcessed
myProcessorHelper(items,
numProcessed + processNextBlockOfItems(items, processedSoFar))
}
}
myProcessorHelper(items, 0)
}
(making it a list of Ints just for simplicity, it would be easy to make it work with a generic List)
I have to say it's one of those cases where I don't mind the mutable variable - it's clear, no reference to it escapes the method.
But as I said in a comment above, processNextBlockOfItems is inherently non-functional anyway, since it's called for its side effects. A more functional way would be for it to return the state of its processing so far, and this state would be updated (and returned) on a subsequent call. Right now, if you in the middle of processing two different items lists, you'd have the issue of maintaining two different partially-processed states within processNextBlockOfItems...
Later:
Still ignoring the state issue, one convenient change would be if processNextBlockOfItems always processed the first block of the items list passed to it, returned the remaining items it had not processed (this is convenient and efficient if using List, so much so I'm wondering why you're using indicies).
This would yield something like:
def myProcessorMoreFunctional(items: List[Int]) {
if (!items.isEmpty) {
myProcessorMoreFunctional(processNextBlockOfItems(items))
}
}
Related
So I was creating an adjacency list from an Undirected Graph
val presentedGraph = listOf(
listOf('i', 'j'),
listOf('k', 'i'),
listOf('m', 'k'),
listOf('k', 'l'),
listOf('o', 'n')
)
The outcome that I was looking for was this
hashMapOf(
'i' to listOf('j', 'k'),
'j' to listOf('i'),
'k' to listOf('i', 'm', 'l'),
'm' to listOf('k'),
'l' to listOf('k'),
'o' to listOf('n'),
'n' to listOf('o')
)
But got this instead
{i=[i], j=[j], k=[k], l=[l], m=[m], n=[n], o=[o]}
Here's the code for it
fun undirectedPath (edges: List<List<Char>>, root: Char, destination: Char){
val graph = buildGraph(edges)
println(graph)
}
fun buildGraph(edges: List<List<Char>>): HashMap<Char, List<Char>>{
val graph = hashMapOf<Char, List<Char>>()
for (i in edges.indices){
for (j in edges[i].indices){
val a = edges[i][j]
val b = edges[i][j]
if (!graph.containsKey(a)) { graph[a] = listOf() }
if (!graph.containsKey(b)) { graph[b] = listOf() }
graph[a] = listOf(b)
graph[b] = listOf(a)
}
}
return graph
}
Any help will be appreciated, Thank You.
Several things wrong here:
The fact that you're setting both a and b to the same expression ought to be a clue that one of them is wrong! In fact a should be set to edges[i][0].
Because j runs from 0, it effectively assumes an extra edge from each node to itself. To avoid that, j should skip the first item and start from 1.
Each time you assign graph[a] and graph[b], you discard any previous items. That's why the result has only one target for each edge. To fix that, you need to add() the target to the existing list…
…which means that each target list must be a MutableList.
Those changes should be enough to get the result you want.
However, there are still several code smells present. For one thing, the input is a list of lists — but each of the inner lists has exactly two items. It would be neater to use a more precise structure, such as a Pair.
And it's always worth being aware of the standard library, which includes a wide range of manipulations and algorithms. In this case, you could replace the whole function with a one-liner:
fun buildGraph(edges: List<Pair<Char, Char>>)
= (edges + edges.map{ it.second to it.first })
.groupBy({ it.first }, { it.second })
As well as being a good deal shorter, that also makes it a good deal clearer what it's doing: combining the list of edges with the reverse list, and returning a map from each node to the list of nodes it connects to/from.
You can try this.
val hashMap = HashMap<Char, ArrayList<Char>>()
presentedGraph.forEach { list ->
list.forEach { char ->
if (!hashMap.containsKey(char)) {
hashMap[char] = arrayListOf()
}
hashMap[char]?.addAll(list.filter { char != it }.toList().distinct())
}
}
println(hashMap)
Output:
{i=[j, k], j=[i], k=[i, m, l], l=[k], m=[k], n=[o], o=[n]}
I stumbled upon this challenge on stackoverflow while learning about property based testing in scala using ScalaCheck.
Find the smallest positive integer that does not occur in a given sequence
I thought of trying to write a generator driven property based test for this problem to check the validity of my program but can't seem to be able to think of a how to write a relevant test case. I understand that I could write a table driven property based testing for this use case but that limit the number of properties I could test this algo with.
import scala.annotation.tailrec
object Solution extends App {
def solution(a: Array[Int]): Int = {
val posNums = a.toSet.filter(_ > 0)
#tailrec
def checkForSmallestNum(ls: Set[Int], nextMin: Int): Int = {
if (ls.contains(nextMin)) checkForSmallestNum(ls, nextMin + 1)
else nextMin
}
checkForSmallestNum(posNums, 1)
}
}
Using Scalatest's (since you did tag scalatest) Scalacheck integration and Scalatest matchers, something like
forAll(Gen.listOf(Gen.posNum[Int]) -> "ints") { ints =>
val asSet = ints.toSet
val smallestNI = Solution.solution(ints.toArray)
asSet shouldNot contain(smallestNI)
// verify that adding non-positive ints doesn't change the result
forAll(
Gen.frequency(
1 -> Gen.const(0),
10 -> Gen.negNum[Int]
) -> "nonPos"
) { nonPos =>
// Adding a non-positive integer to the input shouldn't affect the result
Solution.solution((nonPos :: ints).toArray) shouldBe smallestNI
}
// More of a property-based approach
if (smallestNI > 1) {
forAll(Gen.oneOf(1 until smallestNI) -> "x") { x =>
asSet should contain(x)
}
} else succeed // vacuous
// Alternatively, but perhaps in a less property-based way
(1 until smallestNI).foreach { x =>
asSet should contain(x)
}
}
Note that if scalatest is set to try forAlls 100 times, the nested property check will check values 10k times. Since smallestNI will nearly always be less than the number of trials (as listOf rarely generates long lists), the exhaustive check will in practice be faster than the nested property check.
The overall trick, is that if something is the least positive integer for which some predicate applies, that's the same as saying that for all positive integers less than that something the predicate does not apply.
A tidy number is a number whose digits are in non-decreasing order, e.g. 1234. Here is a way of finding tidy numbers written in Ruby:
def tidy_number(n)
n.to_s.chars.sort.join.to_i == n
end
p tidy_number(12345678) # true
p tidy_number(12345878) # false
I tried to write the same thing in Scala and arrived at the following:
object MyClass {
def tidy_number(n:Int) = n.toString.toList.sorted.mkString.toInt == n;
def main(args: Array[String]) {
println(tidy_number(12345678)) // true
println(tidy_number(12345878)) // false
}
}
The only way I could do it in Scala was by converting an integer to a string to a list and then sorting the list and going back again. My question: is there a better way? 'Better' in the sense there are fewer conversions. I'm mainly looking for a concise piece of Scala but I'd be grateful if someone pointed out a more concise way of doing it in Ruby as well.
You can use sorted on strings in Scala, so
def tidy_number(n: Int) = {
val s = n.toString
s == s.sorted
}
Doing it in two parts also avoids an extra toInt conversion.
I've never used ruby, but this post suggests you're doing it the best way
You can check each adjacent pair of digits to make sure that the first value is <= the second:
def tidy_number(n:Int) =
n.toString.sliding(2,1).forall(p => p(0) <= p(1))
Update following from helpful comments
As noted in the comments, this fails for single-digit numbers. Taking this and another comment together gives this:
def tidy_number(n:Int) =
(" "+n).sliding(2,1).forall(p => p(0) <= p(1))
Being even more pedantic it would be better to convert back to Int before comparing so that you don't rely on the sort order of the characters representing digits being the same as the sort order for the digits themselves.
def tidy_number(n:Int) =
(" "+n).sliding(2,1).forall(p => p(0).toInt <= p(1).toInt)
The only way I could do it in Scala was by converting an integer to a string to a list and then sorting the list and going back again. My question: is there a better way?
String conversion and sorting is not required.
def tidy_number(n :Int) :Boolean =
Iterator.iterate(n)(_/10)
.takeWhile(_>0)
.map(_%10)
.sliding(2)
.forall{case Seq(a,b) => a >= b
case _ => true} //is a single digit "tidy"?
Better? Hard to say. One minor advantage is that the math operations (/ and %) stop after the first false. So for the Int value 1234567890 there are only 3 ops (2 modulo and 1 division) before the result is returned. (Digits are compared in reverse order.)
But seeing as an Int is, at most, only 10 digits long, I'd go with Joel Berkeley's answer just for its brevity.
def tidyNum(n:Int) = {
var t =n; var b = true;
while(b && t!=0){b=t%10>=(t/10)%10;t=t/10};b
}
In Scala REPL:
scala> tidyNum(12345678)
res20: Boolean = true
scala> tidyNum(12343678)
res21: Boolean = false
scala> tidyNum(12)
res22: Boolean = true
scala> tidyNum(1)
res23: Boolean = true
scala> tidyNum(121)
res24: Boolean = false
This can work for any size with just the modification of type such as Long or BigInt in function signature like tidyNum(n:Long) or tidyNum(n:BigInt) as below:
def tidyNum(n:BigInt) = {
var t =n; var b = true;
while(b && t!=0){b=t%10>=(t/10)%10;t=t/10};b
}
scala> tidyNum(BigInt("123456789"))
res26: Boolean = true
scala> tidyNum(BigInt("1234555555555555555555555567890"))
res29: Boolean = false
scala> tidyNum(BigInt("123455555555555555555555556789"))
res30: Boolean = true
And the BigInt can be used for types Int, Long as well.
I am trying to write recursive function in Scala returning first and last element of the list (pair). I'd like to only use .head and .tail without match using simple (not tail) recursive (without defining additional function). Is it possible?
Last element I can find using this code:
def foo(x: List[Int]): (Int) = {
if (x.tail.isEmpty) (x.head)
else foo(x.tail)
}
I'd like to return pair with first and last element (Int, Int). Parameter is (x: List[Int]). It is easy if I pass head as parameter but is it possible without doing it?
You can use a inner, recursive function for the tail, and have an outer function for the head.
However - whether this is still in the spirit of the exercise, is another question.
Here is a very inefficient solution:
def firstAndLast (list: List[Int]): (Int, Int) = {
if (list.isEmpty) (0, 0)
else if (list.size == 1) (list.head, list.head)
else if (list.size == 2)
(list.head, list.tail.head)
else
firstAndLast (list.head :: list.tail.tail)
}
It carries the head not as a separate parameter, but by reappending it at head of the rest of rest of the list, if it is longer than 2.
For a meaningful return for the empty case, I had no good idea. Return Option of tuple in general would be a way.
Another approach
def headTail(l:List[Int]) : (Int, Int) = {
if(l.isEmpty) (-1, -1)
else if(l.tail == List()) (l.head, -1)
else if(l.tail.tail.isEmpty) (l.head, l.tail.head)
else headTail(l.head :: l.tail.tail) //eliminates second element for every iteration
}
I've implemented a basic mutable tree in Scala and I want to traverse it in a functional way in order to search for an element, but I don't know how to implement it. I want also the algorithm to be tail recursive if possible.
The tree is a structure with a value and a list of leafs that are also trees.
Any help would be appreciated.
This is the code that I have (focus on getOpt method):
package main
import scala.collection.mutable.ListBuffer
sealed trait Tree[Node] {
val node: Node
val parent: Option[Tree[Node]]
val children: ListBuffer[Tree[Node]]
def add(n: Node): Tree[Node]
def size: Int
def getOpt(p: (Node) => Boolean): Option[Tree[Node]]
override def toString = {
s"""[$node${if (children.isEmpty) "" else s", Children: $children"}]"""
}
}
case class ConcreteTree(override val node: Int) extends Tree[Int] {
override val children = ListBuffer[Tree[Int]]()
override val parent: Option[Tree[Int]] = None
override def add(n: Int): ConcreteTree = {
val newNode = new ConcreteTree(n) {override val parent: Option[Tree[Int]] = Some(this)}
children += newNode
newNode
}
override def size: Int = {
def _size(t: Tree[Int]): Int = {
1 + t.children.foldLeft(0)((sum, tree) => sum + _size(tree))
}
_size(this)
}
// This method is not correct
override def getOpt(p: (Int) => Boolean): Option[Tree[Int]] = {
def _getOpt(tree: Tree[Int], p: (Int) => Boolean): Option[Tree[Int]] = {
tree.children.map {
t =>
if(p(t.node)) Some(t) else t.children.map(_getOpt(_, p))
}
}
}
}
object Main {
def main(args: Array[String]) {
val tree1 = ConcreteTree(1)
val tree2 = tree1.add(2)
val tree3 = tree1.add(3)
println(tree1.getOpt(_ == 2))
}
}
#doertsch answer is the best approach that I have at the moment.
I would actually go for something more flexible and implement a generic function to produce a lazy stream of your flattened tree, then a lot of your later work will become much easier. Something like this:
def traverse[Node](tree: Tree[Node]): Stream[Tree[Node]] =
tree #:: (tree.children map traverse).fold(Stream.Empty)(_ ++ _)
Then your getOpt reduces to this:
override def getOpt(p: (Int) => Boolean): Option[Tree[Int]] =
traverse(tree) find {x => p(x.node)}
Simplifying even further, if you're just interested in the data without the Tree structure, you can just get a stream of nodes, giving you:
def nodes[Node](tree: Tree[Node]): Stream[Node] = traverse(tree) map (_.node)
def getNode(p: (Int) => Boolean): Option[Int] = nodes(tree) find p
This opens other possibilities for very concise and readable code like nodes(tree) filter (_ > 3), nodes(tree).sum, nodes(tree) contains 12, and similar expressions.
Using the methods exists and find (which every List provides), you can achieve the "finish when a result is found" behaviour. (Although it might be argued that internally, these are not implemented in a totally functional way: https://github.com/scala/scala/blob/5adc400f5ece336f3f5ff19691204975d41e652e/src/library/scala/collection/LinearSeqOptimized.scala#L88)
Your code might look as follows:
case class Tree(nodeValue: Long, children: List[Tree]) {
def containsValue(search: Long): Boolean =
search == nodeValue || children.exists(_.containsValue(search))
def findSubTreeWithNodeValue(search: Long): Option[Tree] =
if (search == nodeValue)
Some(this)
else
children.find(_.containsValue(search)).
flatMap(_.findSubTreeWithNodeValue(search))
}
On the last two lines, the find application will return the correct subtree of the current node, in case one exists, and the flatMap part will extract the correct subtree from it recursively, or leave the None result unchanged if the value was not found.
This one, however, has the unlovely characteristic of doing parts of the traversal twice, once for determining whether a result exists, and once for extracting it from the tree that contains it. There might be a way to fix this in a more efficient way, but I can't figure it out at the moment ...
I think, you are looking for something like this:
#tailrec
def findInTree[T](value: T, stack: List[Node[T]]): Option[Node[T]] = stack match {
case Nil => None
case Node(value) :: _ => stack.headOption
case head :: tail => findInTree(value, head.children ++ tail)
}
This does not traverse parts of the tree twice, and is also tail-recursive.
I changed your class definitions a little bit for clarity, but it's the same idea.
You call it something like this: findInTree(valueToFind, List(root))
When turning a collection into a view, all transformers like map are implemented lazily. This means elements are only processed as required. This should solve your problem:
override def getOpt(p: (Int) => Boolean): Option[Tree[Int]] = {
if (p(node)) Some(this)
else children.view.map(_.getOpt(p)).find(_.isDefined).getOrElse(None)
}
So we are mapping (lazily) over the children, turning them into Options of the searched node. Subsequently we find the first such Option being not None. The final getOrElse(None) is required to "flatten" the nested Options since find returns an Option itself.
I didn't actually run the code, so please excuse minor mistakes. However, the general approach should have become clear.